Non-linear algorithms for noise removal from medical signals using the logical transform

2005 ◽  
Author(s):  
Ethan E. Danahy ◽  
Sos S. Agaian ◽  
Karen A. Panetta
Keyword(s):  
Noise Removal ◽  
Non Linear ◽  
Linear Algorithms ◽  
Medical Signals

scholarly journals Performance of NO, NO<sub>2</sub> low cost sensors and three calibration approaches within a real world application

2018 ◽  
Vol 11 (6) ◽  
pp. 3717-3735 ◽  
Author(s):  
Alessandro Bigi ◽  
Michael Mueller ◽  
Stuart K. Grange ◽  
Grazia Ghermandi ◽  
Christoph Hueglin
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Support Vector Regression ◽  
Low Cost ◽  
Multivariate Linear Regression ◽  
Pollution Level ◽  
Support Vector ◽  
Non Linear ◽  
Linear Algorithms ◽  
Urban Sites

Abstract. Low cost sensors for measuring atmospheric pollutants are experiencing an increase in popularity worldwide among practitioners, academia and environmental agencies, and a large amount of data by these devices are being delivered to the public. Notwithstanding their behaviour, performance and reliability are not yet fully investigated and understood. In the present study we investigate the medium term performance of a set of NO and NO2 electrochemical sensors in Switzerland using three different regression algorithms within a field calibration approach. In order to mimic a realistic application of these devices, the sensors were initially co-located at a rural regulatory monitoring site for a 4-month calibration period, and subsequently deployed for 4 months at two distant regulatory urban sites in traffic and urban background conditions, where the performance of the calibration algorithms was explored. The applied algorithms were Multivariate Linear Regression, Support Vector Regression and Random Forest; these were tested, along with the sensors, in terms of generalisability, selectivity, drift, uncertainty, bias, noise and suitability for spatial mapping intra-urban pollution gradients with hourly resolution. Results from the deployment at the urban sites show a better performance of the non-linear algorithms (Support Vector Regression and Random Forest) achieving RMSE  <  5 ppb, R2 between 0.74 and 0.95 and MAE between 2 and 4 ppb. The combined use of both NO and NO2 sensor output in the estimate of each pollutant showed some contribution by NO sensor to NO2 estimate and vice-versa. All algorithms exhibited a drift ranging between 5 and 10 ppb for Random Forest and 15 ppb for Multivariate Linear Regression at the end of the deployment. The lowest concentration correctly estimated, with a 25 % relative expanded uncertainty, resulted in ca. 15–20 ppb and was provided by the non-linear algorithms. As an assessment for the suitability of the tested sensors for a targeted application, the probability of resolving hourly concentration difference in cities was investigated. It was found that NO concentration differences of 5–10 ppb (8–10 for NO2) can reliably be detected (90 % confidence), depending on the air pollution level. The findings of this study, although derived from a specific sensor type and sensor model, are based on a flexible methodology and have extensive potential for exploring the performance of other low cost sensors, that are different in their target pollutant and sensing technology.

scholarly journals Benchmarking algorithms for genomic prediction of complex traits

2019 ◽  
Author(s):  
Christina B. Azodi ◽  
Andrew McCarren ◽  
Mark Roantree ◽  
Gustavo de los Campos ◽  
Shin-Han Shiu
Keyword(s):  
Feature Selection ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Algorithm Selection ◽  
Livestock Breeding ◽  
Wide Range ◽  
Non Linear ◽  
Hyperparameter Selection ◽  
Model Training ◽  
Linear Algorithms

AbstractThe usefulness of Genomic Prediction (GP) in crop and livestock breeding programs has led to efforts to develop new and improved GP approaches including non-linear algorithm, such as artificial neural networks (ANN) (i.e. deep learning) and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of GP datasets and models. Using data of 18 traits across six plant species with different marker densities and training population sizes, we compared the performance of six linear and five non-linear algorithms, including ANNs. First, we found that hyperparameter selection was critical for all non-linear algorithms and that feature selection prior to model training was necessary for ANNs when the markers greatly outnumbered the number of training lines. Across all species and trait combinations, no one algorithm performed best, however predictions based on a combination of results from multiple GP algorithms (i.e. ensemble predictions) performed consistently well. While linear and non-linear algorithms performed best for a similar number of traits, the performance of non-linear algorithms vary more between traits than that of linear algorithms. Although ANNs did not perform best for any trait, we identified strategies (i.e. feature selection, seeded starting weights) that boosted their performance near the level of other algorithms. These results, together with the fact that even small improvements in GP performance could accumulate into large genetic gains over the course of a breeding program, highlights the importance of algorithm selection for the prediction of trait values.

A Non-Linear Approach to ECG Signal Processing using Morphological Filters

2013 ◽  
Vol 3 (3) ◽  
pp. 46-59 ◽  
Author(s):  
Vikrant Bhateja ◽  
Rishendra Verma ◽  
Rini Mehrotra ◽  
Shabana Urooj
Keyword(s):  
Signal Processing ◽  
Noise Removal ◽  
High Signal ◽  
Ecg Signal ◽  
Processing Scheme ◽  
Baseline Drift ◽  
Ecg Signals ◽  
Non Linear ◽  
Ecg Signal Processing ◽  
Electrocardiogram Ecg

Analysis of the Electrocardiogram (ECG) signals is the pre-requisite for the clinical diagnosis of cardiovascular diseases. ECG signal is degraded by artifacts such as baseline drift and noises which appear during the acquisition phase. The effect of impulse and Gaussian noises is randomly distributed whereas baseline drift generally affects the baseline of the ECG signal; these artifacts induce interference in the diagnosis of cardio diseases. The influence of these artifacts on the ECG signals needs to be removed by suitable ECG signal processing scheme. This paper proposes combination of non linear morphological operators for the noise and baseline drift removal. Non flat structuring elements of varying dimensions are employed with morphological filtering to achieve low distortion as well as good noise removal. Simulation outcomes illustrate noteworthy improvement in baseline drift yielding lower values of MSE and PRD; on the other hand high signal to noise ratios depicts suppression of impulse and Gaussian noises.

Non-linear fourth-order telegraph-diffusion equation for noise removal

2013 ◽  
Vol 7 (4) ◽  
pp. 335-342 ◽  
Author(s):  
Weili Zeng ◽  
Xianghua Tan ◽  
Xiaobo Lu
Keyword(s):  
Diffusion Equation ◽  
Fourth Order ◽  
Noise Removal ◽  
Non Linear
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